In coastal zone management, the U.S. government provides guidance to the states, which then implement the objectives of the Coastal Zone Management Act of 1972 and Federal Water Pollution Control Amendments of 1972, Clean Water Act of 1977, and the Water Quality Act of 1987. To enhance state and federal decision-making processes related to the development of outer continental shelf energy and mineral resources, potential environmental impacts from exploring and extracting resources (such as oil and gas) in compliance with numerous environmental statues, regulations, and executive orders (e.g., OCSLA and NEPA) need to be assessed. Size, timing, and location of future lease sales are all issues with respect to resource exploration and extraction.
Currently, ecological forecasting used in the decision-making process for ecosystem and resource management frequently relies upon historical in-situ measurements (often presented as climatological products), earth observations (EO) from remote sensing platforms, or various types of models. Each of these elements has inherent limitations and errors. In-situ data, expensive and time-consuming to collect, frequently contains many gaps and are subject to temporal and spatial aliasing. Methods to reduce theses effects result in coarse grained, low temporal resolution products. Climatologies are also generally of low spatial and temporal resolution (monthly, seasonal, or annual means). Remote sensing products (EO), as from Ocean Color satellites (CZCS, SeaWiFS, MODIS, MERIS, OCM, etc.), provide daily, high-resolution data sets. These data, however, have their own limitations, such as data gaps caused by cloud coverage and contamination of the signal in near-shore environments by atmospheric aerosols, bottom reflectance, and contamination from coastal runoff. Coastal waters, rich in admixed organic and inorganic material, require sophisticated, but subjective remote-sensing algorithms to deconvolve the individual constituents. These instruments measure an integrated signal from the upper ocean, often missing ecologically important subsurface layers. Models too have their limits. They are based upon assumptions and simplifications that introduce errors and biases. Models often require specialized skill and knowledge to set-up, fine-tune, and execute; additionally, they usually require high-performance computing resources. Furthermore, models require initialization and boundary conditions at different spatial and temporal scales. For example, fisheries models require temperature and chlorophyll fields over a large, coarse grid, while oyster reef models require temperature, salinity, currents, and chlorophyll on a small spatial scale but with high spatial resolution.